Following meeting with Ilan
Jonathan Harrison
9 October 2018
Notes following meeting with Ilan on 8th October.
Include observed data in Figure 7 or similar figure. Show unnormalised. Also quantify in WT and OE the number and fraction of transcripts that are localized. Demonstrate that particles are equivalent between WT and the OE mutant.
To investigate: Inhomogeneous production due to patchy expression of the gal-4 driver. Inhomogeneous transport through ring canals. Waiting of mRNA to form into complexes at the nuclear pore complex, such as for assembly with dynein or other proteins. (Model comparison between some of these are the ‘normal’ model).
Observed data
Observed distribution of RNA across cells
To illustrate these ideas, we plot the RNA distributions for WT and OE based on observed data. 
The same thing but without the normalisation:
Show the number and fraction of complexes localized
These plots of the observed data suggest that the fraction of complexes localized is different between wild type and overexpressor. The number of transcripts localized also appeasrs lower in the overexpression mutant, but in my opinion this is due to the age/size of the egg chambers considered. The egg chambers quantified are generally smaller. (Possible bias here as there are too many particles in the larger overexpression datasets so that MATLAB runs out of memory in running the FISH-quant code to localize the transcripts.)
How many times greater is production in the overexpressor than in wild type?
We fit a linear model to the data on total RNA counts in each egg chamber for each phenotype. If \[z = \sum_{i=1}^{16} y_i\] is the total RNA count in each egg chamber, then \[\frac{\text{d} z}{\text{d} t} = 15a.\] Therefore \[ z = z_0 + 15at.\]
Based on the total RNA counts in each egg chamber, we can estimate production rate \(a\) directly for WT and OE data and give a ratio of how much bigger the production for the OE is.
## [1] 9.714501## [1] 24.57184## [1] 2.529398This neglects effects from assembly in the oocyte, which we ought to account for.
## [1] 17.74595## [1] 32.81549## [1] 1.849182Based on this it seems reasonable to keep using \(2a\) as the production in the overexpressor.
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [2,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [3,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [4,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [5,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [6,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [7,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [8,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [9,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [,14] [,15] [,16]
## [1,] 2 2 2
## [2,] 2 2 2
## [3,] 2 2 2
## [4,] 2 2 2
## [5,] 2 2 2
## [6,] 2 2 2
## [7,] 2 2 2
## [8,] 2 2 2
## [9,] 2 2 2
## [,1] [,2] [,3]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -6719.1 5505.6
## p_loo 766.9 624.1
## looic 13438.3 11011.2
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1 11.1% 788
## (0.5, 0.7] (ok) 1 11.1% 354
## (0.7, 1] (bad) 1 11.1% 171
## (1, Inf) (very bad) 6 66.7% 1
## See help('pareto-k-diagnostic') for details.
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [2,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [3,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [4,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [5,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [6,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [7,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [8,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [9,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [,14] [,15] [,16]
## [1,] 2 2 2
## [2,] 2 2 2
## [3,] 2 2 2
## [4,] 2 2 2
## [5,] 2 2 2
## [6,] 2 2 2
## [7,] 2 2 2
## [8,] 2 2 2
## [9,] 2 2 2
## [,1] [,2] [,3]
## [1,] 6 9 1
## [2,] 16 1 1
## [3,] 12 16 1
## [4,] 10 11 1
## [5,] 16 1 1
## [6,] 10 1 1
## [7,] 1 1 1
## [8,] 6 11 16
## [9,] 6 11 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -6670.7 5497.0
## p_loo 758.8 644.3
## looic 13341.4 10994.0
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1 11.1% 774
## (0.5, 0.7] (ok) 2 22.2% 283
## (0.7, 1] (bad) 1 11.1% 15
## (1, Inf) (very bad) 5 55.6% 1
## See help('pareto-k-diagnostic') for details.
## [1] TRUE TRUE
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [2,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [3,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [4,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [5,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [6,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [7,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [8,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [9,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [,14] [,15] [,16]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
## [,1] [,2] [,3]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -7116.7 5817.1
## p_loo 983.3 766.9
## looic 14233.4 11634.3
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1 11.1% 850
## (0.5, 0.7] (ok) 0 0.0% <NA>
## (0.7, 1] (bad) 1 11.1% 46
## (1, Inf) (very bad) 7 77.8% 1
## See help('pareto-k-diagnostic') for details.
## [1] 2 2
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [2,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [3,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [4,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [5,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [6,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [7,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [8,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [9,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [,14] [,15] [,16]
## [1,] 2 2 2
## [2,] 2 2 2
## [3,] 2 2 2
## [4,] 2 2 2
## [5,] 2 2 2
## [6,] 2 2 2
## [7,] 2 2 2
## [8,] 2 2 2
## [9,] 2 2 2
## [,1] [,2] [,3]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -6596.8 5534.3
## p_loo 780.0 727.8
## looic 13193.5 11068.5
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 3 33.3% 294
## (0.5, 0.7] (ok) 0 0.0% <NA>
## (0.7, 1] (bad) 2 22.2% 199
## (1, Inf) (very bad) 4 44.4% 1
## See help('pareto-k-diagnostic') for details.
## elpd_diff se
## 48.4 19.7## elpd_diff se
## -397.6 314.1## elpd_diff se
## -446.0 323.5## elpd_diff elpd_loo se_elpd_loo p_loo se_p_loo looic
## res_prod4a[[2]] 0.0 -6596.8 5534.3 780.0 727.8 13193.5
## res_block[[2]] -73.9 -6670.7 5497.0 758.8 644.3 13341.4
## res_simple[[2]] -122.4 -6719.1 5505.6 766.9 624.1 13438.3
## res_prod[[2]] -519.9 -7116.7 5817.1 983.3 766.9 14233.4
## se_looic
## res_prod4a[[2]] 11068.5
## res_block[[2]] 10994.0
## res_simple[[2]] 11011.2
## res_prod[[2]] 11634.3
Following meeting with Ruth 2018/10/11
We will consider the following models: 0. Simple (M0) 1. Blocked RCs (M1) 2. Inhomogeneous production (2a,a) (M2) 3. Inhomogeneous production (4a,2a) (M3) 4. Inhomogeneous production (4a,a) (M4) 5. Inhomogeneous production (ka,a) (M5) 6. Blocking and production (M6) 7. Density dependent transport (M7) 8. Density dependent transport and production (M8) 9. Density dependent transport and blocking (M9)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [2,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [3,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [4,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [5,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [6,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [7,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [8,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [9,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [,14] [,15] [,16]
## [1,] 2 2 2
## [2,] 2 2 2
## [3,] 2 2 2
## [4,] 2 2 2
## [5,] 2 2 2
## [6,] 2 2 2
## [7,] 2 2 2
## [8,] 2 2 2
## [9,] 2 2 2
## [,1] [,2] [,3]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -6719.1 5505.6
## p_loo 766.9 624.1
## looic 13438.3 11011.2
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1 11.1% 788
## (0.5, 0.7] (ok) 1 11.1% 354
## (0.7, 1] (bad) 1 11.1% 171
## (1, Inf) (very bad) 6 66.7% 1
## See help('pareto-k-diagnostic') for details.
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [2,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [3,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [4,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [5,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [6,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [7,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [8,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [9,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [,14] [,15] [,16]
## [1,] 2 2 2
## [2,] 2 2 2
## [3,] 2 2 2
## [4,] 2 2 2
## [5,] 2 2 2
## [6,] 2 2 2
## [7,] 2 2 2
## [8,] 2 2 2
## [9,] 2 2 2
## [,1] [,2] [,3]
## [1,] 6 9 1
## [2,] 16 1 1
## [3,] 12 16 1
## [4,] 10 11 1
## [5,] 16 1 1
## [6,] 10 1 1
## [7,] 1 1 1
## [8,] 6 11 16
## [9,] 6 11 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -6670.7 5497.0
## p_loo 758.8 644.3
## looic 13341.4 10994.0
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1 11.1% 774
## (0.5, 0.7] (ok) 2 22.2% 283
## (0.7, 1] (bad) 1 11.1% 15
## (1, Inf) (very bad) 5 55.6% 1
## See help('pareto-k-diagnostic') for details.
## [1] TRUE TRUE
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [2,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [3,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [4,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [5,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [6,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [7,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [8,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [9,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [,14] [,15] [,16]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
## [,1] [,2] [,3]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -7116.7 5817.1
## p_loo 983.3 766.9
## looic 14233.4 11634.3
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1 11.1% 850
## (0.5, 0.7] (ok) 0 0.0% <NA>
## (0.7, 1] (bad) 1 11.1% 46
## (1, Inf) (very bad) 7 77.8% 1
## See help('pareto-k-diagnostic') for details.
## [1] 2 2
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [2,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [3,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [4,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [5,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [6,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [7,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [8,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [9,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [,14] [,15] [,16]
## [1,] 2 2 2
## [2,] 2 2 2
## [3,] 2 2 2
## [4,] 2 2 2
## [5,] 2 2 2
## [6,] 2 2 2
## [7,] 2 2 2
## [8,] 2 2 2
## [9,] 2 2 2
## [,1] [,2] [,3]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -6596.8 5534.3
## p_loo 780.0 727.8
## looic 13193.5 11068.5
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 3 33.3% 294
## (0.5, 0.7] (ok) 0 0.0% <NA>
## (0.7, 1] (bad) 2 22.2% 199
## (1, Inf) (very bad) 4 44.4% 1
## See help('pareto-k-diagnostic') for details.
## [1] 4 1
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [2,] 0 1 1 4 1 4 4 4 1 4 4 4 4
## [3,] 0 1 1 4 1 1 4 4 1 1 4 4 1
## [4,] 0 4 1 4 1 4 1 1 1 1 1 4 4
## [5,] 0 1 1 1 1 1 4 1 1 1 4 1 1
## [6,] 0 1 1 4 1 1 4 4 1 4 4 4 4
## [7,] 0 1 1 4 1 4 4 4 1 4 4 1 4
## [8,] 0 1 1 4 1 1 4 4 1 1 4 4 4
## [9,] 0 1 1 4 1 1 4 4 1 1 4 4 4
## [,14] [,15] [,16]
## [1,] 1 1 1
## [2,] 4 4 4
## [3,] 1 4 4
## [4,] 1 4 4
## [5,] 1 1 1
## [6,] 1 4 4
## [7,] 4 4 4
## [8,] 1 4 4
## [9,] 1 4 4
## [,1] [,2] [,3]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -6932.1 5811.5
## p_loo 782.0 680.8
## looic 13864.1 11623.1
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 2 22.2% 1411
## (0.5, 0.7] (ok) 1 11.1% 388
## (0.7, 1] (bad) 3 33.3% 17
## (1, Inf) (very bad) 3 33.3% 1
## See help('pareto-k-diagnostic') for details.
## [1] "fitting difference between producers in OE and WT from data"
## [1] -1 1
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [2,] 0 1 1 -1 1 -1 -1 -1 1 -1 -1 -1 -1
## [3,] 0 1 1 -1 1 1 -1 -1 1 1 -1 -1 1
## [4,] 0 -1 1 -1 1 -1 1 1 1 1 1 -1 -1
## [5,] 0 1 1 1 1 1 -1 1 1 1 -1 1 1
## [6,] 0 1 1 -1 1 1 -1 -1 1 -1 -1 -1 -1
## [7,] 0 1 1 -1 1 -1 -1 -1 1 -1 -1 1 -1
## [8,] 0 1 1 -1 1 1 -1 -1 1 1 -1 -1 -1
## [9,] 0 1 1 -1 1 1 -1 -1 1 1 -1 -1 -1
## [,14] [,15] [,16]
## [1,] 1 1 1
## [2,] -1 -1 -1
## [3,] 1 -1 -1
## [4,] 1 -1 -1
## [5,] 1 1 1
## [6,] 1 -1 -1
## [7,] -1 -1 -1
## [8,] 1 -1 -1
## [9,] 1 -1 -1
## [,1] [,2] [,3]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -2711.2 1114.4
## p_loo 224.4 193.3
## looic 5422.5 2228.7
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 0 0.0% <NA>
## (0.5, 0.7] (ok) 8 88.9% 147
## (0.7, 1] (bad) 0 0.0% <NA>
## (1, Inf) (very bad) 1 11.1% 1
## See help('pareto-k-diagnostic') for details.
## [1] 4 1
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [2,] 0 1 1 4 1 4 4 4 1 4 4 4 4
## [3,] 0 1 1 4 1 1 4 4 1 1 4 4 1
## [4,] 0 4 1 4 1 4 1 1 1 1 1 4 4
## [5,] 0 1 1 1 1 1 4 1 1 1 4 1 1
## [6,] 0 1 1 4 1 1 4 4 1 4 4 4 4
## [7,] 0 1 1 4 1 4 4 4 1 4 4 1 4
## [8,] 0 1 1 4 1 1 4 4 1 1 4 4 4
## [9,] 0 1 1 4 1 1 4 4 1 1 4 4 4
## [,14] [,15] [,16]
## [1,] 1 1 1
## [2,] 4 4 4
## [3,] 1 4 4
## [4,] 1 4 4
## [5,] 1 1 1
## [6,] 1 4 4
## [7,] 4 4 4
## [8,] 1 4 4
## [9,] 1 4 4
## [,1] [,2] [,3]
## [1,] 6 9 1
## [2,] 16 1 1
## [3,] 12 16 1
## [4,] 10 11 1
## [5,] 16 1 1
## [6,] 10 1 1
## [7,] 1 1 1
## [8,] 6 11 16
## [9,] 6 11 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -6938.2 5818.3
## p_loo 748.3 652.1
## looic 13876.4 11636.6
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 2 22.2% 1877
## (0.5, 0.7] (ok) 3 33.3% 143
## (0.7, 1] (bad) 1 11.1% 15
## (1, Inf) (very bad) 3 33.3% 1
## See help('pareto-k-diagnostic') for details.
## [1] 2 2
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [2,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [3,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [4,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [5,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [6,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [7,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [8,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [9,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [,14] [,15] [,16]
## [1,] 2 2 2
## [2,] 2 2 2
## [3,] 2 2 2
## [4,] 2 2 2
## [5,] 2 2 2
## [6,] 2 2 2
## [7,] 2 2 2
## [8,] 2 2 2
## [9,] 2 2 2
## [,1] [,2] [,3]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -6499.5 5141.6
## p_loo 937.4 698.8
## looic 12999.0 10283.2
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 0 0.0% <NA>
## (0.5, 0.7] (ok) 1 11.1% 152
## (0.7, 1] (bad) 1 11.1% 38
## (1, Inf) (very bad) 7 77.8% 1
## See help('pareto-k-diagnostic') for details.
## [1] 4 1
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 1 1 1 1 1 1 1 1 1 1 1 1
## [2,] 0 1 1 4 1 4 4 4 1 4 4 4 4
## [3,] 0 1 1 4 1 1 4 4 1 1 4 4 1
## [4,] 0 4 1 4 1 4 1 1 1 1 1 4 4
## [5,] 0 1 1 1 1 1 4 1 1 1 4 1 1
## [6,] 0 1 1 4 1 1 4 4 1 4 4 4 4
## [7,] 0 1 1 4 1 4 4 4 1 4 4 1 4
## [8,] 0 1 1 4 1 1 4 4 1 1 4 4 4
## [9,] 0 1 1 4 1 1 4 4 1 1 4 4 4
## [,14] [,15] [,16]
## [1,] 1 1 1
## [2,] 4 4 4
## [3,] 1 4 4
## [4,] 1 4 4
## [5,] 1 1 1
## [6,] 1 4 4
## [7,] 4 4 4
## [8,] 1 4 4
## [9,] 1 4 4
## [,1] [,2] [,3]
## [1,] 1 1 1
## [2,] 1 1 1
## [3,] 1 1 1
## [4,] 1 1 1
## [5,] 1 1 1
## [6,] 1 1 1
## [7,] 1 1 1
## [8,] 1 1 1
## [9,] 1 1 1
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -6606.0 5418.9
## p_loo 862.1 713.6
## looic 13212.0 10837.9
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 0 0.0% <NA>
## (0.5, 0.7] (ok) 0 0.0% <NA>
## (0.7, 1] (bad) 4 44.4% 13
## (1, Inf) (very bad) 5 55.6% 1
## See help('pareto-k-diagnostic') for details.
## [1] "using real data \n"
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [2,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [3,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [4,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [5,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [6,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [7,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [8,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [9,] 0 2 2 2 2 2 2 2 2 2 2 2 2
## [,14] [,15] [,16]
## [1,] 2 2 2
## [2,] 2 2 2
## [3,] 2 2 2
## [4,] 2 2 2
## [5,] 2 2 2
## [6,] 2 2 2
## [7,] 2 2 2
## [8,] 2 2 2
## [9,] 2 2 2
## [,1] [,2] [,3]
## [1,] 6 9 1
## [2,] 16 1 1
## [3,] 12 16 1
## [4,] 10 11 1
## [5,] 16 1 1
## [6,] 10 1 1
## [7,] 1 1 1
## [8,] 6 11 16
## [9,] 6 11 1
## Inference for Stan model: mrna_transport_density_dependent_with_blocking.
## 4 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=4000.
##
## mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
## a 12.15 0.04 2.05 8.93 10.74 11.88 13.32 17.02 2112 1
## b 14.55 0.16 6.35 4.26 10.04 13.76 18.53 28.96 1673 1
## nu 0.18 0.00 0.05 0.12 0.15 0.17 0.20 0.29 1111 1
## phi 0.38 0.00 0.05 0.29 0.35 0.38 0.41 0.47 2775 1
## sigma 1.30 0.00 0.14 1.05 1.20 1.29 1.39 1.58 2588 1
## beta 0.02 0.00 0.01 0.01 0.01 0.02 0.02 0.03 1635 1
##
## Samples were drawn using NUTS(diag_e) at Sun Oct 14 23:51:21 2018.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).
##
## Computed from 4000 by 9 log-likelihood matrix
##
## Estimate SE
## elpd_loo -6427.9 5157.8
## p_loo 898.7 733.2
## looic 12855.9 10315.7
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 0 0.0% <NA>
## (0.5, 0.7] (ok) 1 11.1% 229
## (0.7, 1] (bad) 2 22.2% 27
## (1, Inf) (very bad) 6 66.7% 1
## See help('pareto-k-diagnostic') for details.
## elpd_diff elpd_loo se_elpd_loo p_loo se_p_loo looic
## res_M5[[2]] 0.0 -2711.2 1114.4 224.4 193.3 5422.5
## res_M9[[2]] -3716.7 -6427.9 5157.8 898.7 733.2 12855.9
## res_M7[[2]] -3788.2 -6499.5 5141.6 937.4 698.8 12999.0
## res_M3[[2]] -3885.5 -6596.8 5534.3 780.0 727.8 13193.5
## res_M8[[2]] -3894.8 -6606.0 5418.9 862.1 713.6 13212.0
## res_M1[[2]] -3959.5 -6670.7 5497.0 758.8 644.3 13341.4
## res_M0[[2]] -4007.9 -6719.1 5505.6 766.9 624.1 13438.3
## res_M4[[2]] -4220.8 -6932.1 5811.5 782.0 680.8 13864.1
## res_M6[[2]] -4226.9 -6938.2 5818.3 748.3 652.1 13876.4
## res_M2[[2]] -4405.4 -7116.7 5817.1 983.3 766.9 14233.4
## se_looic
## res_M5[[2]] 2228.7
## res_M9[[2]] 10315.7
## res_M7[[2]] 10283.2
## res_M3[[2]] 11068.5
## res_M8[[2]] 10837.9
## res_M1[[2]] 10994.0
## res_M0[[2]] 11011.2
## res_M4[[2]] 11623.1
## res_M6[[2]] 11636.6
## res_M2[[2]] 11634.3## elpd_diff elpd_loo se_elpd_loo p_loo se_p_loo looic
## res_M9[[2]] 0.0 -6427.9 5157.8 898.7 733.2 12855.9
## res_M7[[2]] -71.6 -6499.5 5141.6 937.4 698.8 12999.0
## res_M3[[2]] -168.8 -6596.8 5534.3 780.0 727.8 13193.5
## res_M8[[2]] -178.1 -6606.0 5418.9 862.1 713.6 13212.0
## res_M1[[2]] -242.8 -6670.7 5497.0 758.8 644.3 13341.4
## res_M0[[2]] -291.2 -6719.1 5505.6 766.9 624.1 13438.3
## res_M4[[2]] -504.1 -6932.1 5811.5 782.0 680.8 13864.1
## res_M6[[2]] -510.3 -6938.2 5818.3 748.3 652.1 13876.4
## res_M2[[2]] -688.8 -7116.7 5817.1 983.3 766.9 14233.4
## se_looic
## res_M9[[2]] 10315.7
## res_M7[[2]] 10283.2
## res_M3[[2]] 11068.5
## res_M8[[2]] 10837.9
## res_M1[[2]] 10994.0
## res_M0[[2]] 11011.2
## res_M4[[2]] 11623.1
## res_M6[[2]] 11636.6
## res_M2[[2]] 11634.3
TO DO: Run M9 (Done) Rerun M5 to figure out what went on here (or leave this). Run some/all ommitting young egg chambers or investigating timescale model to try to avoid issues due to this